1

BEAUTY IS WEALTH: CEO APPEARANCE AND SHAREHOLDER VALUE

Joseph T. Halford and Hung-Chia Hsu*

Abstract

This paper examines whether and how the appearance of chief executives officers (CEOs) is

related to shareholder value. We obtain a Facial Attractiveness Index of 682 CEOs from the S&P

500 companies based on their facial geometry. CEOs with a higher Facial Attractiveness Index

are associated with better returns around their job announcements, and higher acquirer returns

upon acquisition announcements. To mitigate endogeneity concerns, we compare stock returns

surrounding CEO television news events with those surrounding a matched sample of news

article events related to the same CEO. CEOs Facial Attractiveness Index positively affects the

stock returns on the television interview date, but not around the news report date. The findings

suggest that CEO appearance matters for shareholder value and provide an explanation why

more attractive CEOs receive beauty premiums in their compensation.

*Halford and Hsu are from University of Wisconsin Milwaukee. Halfords contact information: halford@uwm.edu.

Hsus contact information: hsuh@uwm.edu.

Acknowledgement: We thank Hank Bessembinder, Mike Cooper, Mara Faccio, Dick Marcus, Lilian Ng, Val

Sibilkov, Jin Xu and seminar participants at University of Wisconsin Milwaukee for their valuable comments. Fei

Liu and Hanna Veiga provided excellent research assistance. All remaining errors are our own.

mailto:halford@uwm.edumailto:hsuh@uwm.edu

2

This paper examines whether and how chief executive officers (CEOs) appearance relates to

shareholder value. Appearance, measured by sheer beauty, perceived competence, likability, and

trustworthiness, is associated with various types of individual outcomes. It predicts candidates

election results (Todorov et al. (2005), among others), individual income, achievements, peer

recognition (Hamermesh and Biddle (1994), Kennedy (1990)), and even military ranks (Mazur et

al. (1984)). In the finance literature, perceived competence and attractiveness affect managerial

compensation (Graham, Harvey and Puri (2010)), personal lending (Duarte, Siegel, and Young

(2012), Ravina (2012)), and hedge fund investments (Pareek and Zuckerman (2013)). However,

despite the documented effects of appearance on CEO pay and financing activities at the

personal level, it is far less clear whether and how appearance is related to shareholder value.

Graham, Harvey, and Puri (2010) find no evidence that firms of competent looking CEOs

achieve better performance, where performance is measured by return on assets. In a competitive

labor market, given the evidence that better looking CEOs receive higher pay, we would expect

that more attractive CEOs contribute to shareholder value in some way(s).

To further assess whether and in what channels CEO appearance is associated with

shareholder value, we obtain a Facial Attractiveness Index of 682 CEOs of S&P 500 companies

based on their facial geometry. We use facial geometry as the measure of CEO attractiveness for

the following reasons. First, since the time of ancient Greece, a persons facial geometry,

including the golden ratio, has been well documented to relate to beauty and attractiveness.1 In

the psychology literature, Rhodes (2006), among others, finds that facial averageness and

symmetry indeed indicate attractiveness in both male and female faces and across cultures.

Second, facial geometry, which is a biologically based standard of beauty, appears to be a more

1 See, for example, the discussion of an ABC News article titled Britains Most Beautiful Face Reveals Beauty

Secrets. (http://abcnews.go.com/blogs/lifestyle/2012/04/britains-most-beautiful-face-reveals-beauty-secrets/)

http://abcnews.go.com/blogs/lifestyle/2012/04/britains-most-beautiful-face-reveals-beauty-secrets/

3

persistent measure of attractiveness and invites less selective perception bias commonly seen in

survey-based measures.2 Finally, facial geometry based measures are easy to quantify using

geometry and mathematics. We obtain each CEOs Facial Attractive Index from Anaface.com, a

web-based photo analysis application that computes a facial beauty score according to a persons

facial geometry. The construction of this score is based on scientific research, various elements

of neoclassical beauty, and statistical analysis.

Our findings are summarized as follows. First, more attractive CEOs are associated with

better stock returns around their job announcements. This effect appears to be economically

significant: A ten percent increase in CEOs Facial Attractiveness Index relates to a 1.37%

increase in abnormal returns within ten days surrounding the job announcement dates. This result

provides the initial evidence that CEO appearance enhances shareholder value and that more

attractive CEOs seem to gain a first impression advantage in stock prices. We then propose

and test two channels through which CEO appearance matters for shareholder value: negotiating

and visibility. Existing evidence suggests that more physically attractive people are better

communicators and negotiators; they thus receive a greater surplus in negotiation (Chaiken

(1979), Rosenblatt (2008)). We examine a key corporate event on which CEOs interpersonal

and negotiating skills are extremely importantmergers and acquisitions (M&As).3 We find a

positive and significant relation between CEO attractiveness and acquirer returns around merger

announcement dates. In particular, a ten percent increase in CEOs Facial Attractiveness Index

leads to a 1.31% increase in abnormal returns within ten days surrounding the merger

announcement dates. This positive relation persists even after one year following the mergers

2 For example, using a two-person sequential trust game, DeBruine (2002) finds that subjects are more likely to trust

partners who show more facial resemblance to subjects themselves. 3 The Wall Street Journal, 8/21/2006, Best acquisitions start with charming CEO. The article wrote There is no

substitute for establishing good personal rapport with sellersAs they see it, their biggest edge comes not from

what they do in the boardroom, but from getting on the road and wooing possible sellers.

4

announcement, supporting the argument that more attractive CEOs enhance shareholder value

through better negotiating prowess.

Finally, CEO attractiveness may also relate to shareholder value through the visibility

channel, in which media attention affects a firms investor base and stock prices (Merton (1987),

Kim and Meschke (2013)). If visibility is an important determinant of stock prices, firms may

hire more attractive CEOs, ceteris paribus, to help enhance their images. Consistent with the

visibility channel, more attractive CEOs are associated with better stock returns on CEO-related

television news days. To mitigate potential endogeneity concerns, we compare the effects of

facial attractiveness on stock returns around CEOs television news events to the effect of facial

attractiveness on returns surrounding a matched sample of non-television news events for the

same group of CEOs. CEO attractiveness has no significant impacts on stock returns around the

matched sample of non-television news event dates. This finding helps mitigate endogeneity

concerns and further supports the visibility hypothesis. Overall, our findings suggest that more

attractive CEOs receive higher compensation for a reason: They create value for shareholders

through better negotiating power and visibility.

This paper contributes to the literature on the effects of individuals physical attributes on

economic outcomes. In addition to the aforementioned studies on facial attractiveness, an

individuals height, body mass index and other physical features are shown to affect his or her

social experiences, wages, labor market outcomes, and investing behavior. For example, Persico,

Postlewaite, and Silverman (2004) find that taller workers receive a wage premium. Further,

Addoum, Korniotis, and Kumar (2013) show that individuals who are tall and of normal weight

relative to their peers are more likely to participate in financial markets and hold risker portfolios.

The existing literature along this line focuses on the relation between physical attributes and

5

individual level outcomes. In contrast, the current study examines a special group of

individualsCEOs, and finds evidence that an individuals physical attractiveness may also

affect group welfare such as shareholder value. Focusing on CEOs provides a good setting for

testing the effects of individuals attractiveness on social economic outcomes, as CEOs have a

considerable influence on corporate policies and thus shareholder value.

Our paper also relates to a large literature on the effects of CEO on corporate outcomes.

Existing literature finds that manager fixed effects matter (Bertrand and Schoar (2003), Graham,

Li and Qiu (2011)). Further, characteristics of CEOs, including gender (Faccio, Marchica, and

Mura (2012)), overconfidence (Malmendier and Tate (2005, 2008), Malmendier, Tate and Yan

(2011)), their psychological traits, attitudes (Graham, Harvey, and Puri (2013)), affective states

(Mayew and Venkatachalam (2012)), and their various abilities and skills (Kaplan, Klebanov,

and Sorensen (2012)), matter for firm investment and success. A broad psychology literature

suggests that personality is manifested through appearance (Naumann, Vazire, Rentfrow, and

Gosling (2009), among others), but there is much less literature on how appearance affects

corporate activities. This paper adds to this literature by providing novel findings that CEO

appearance matters for shareholder value through the negotiating and visibility channels.

Finally, the present study contributes to the literature on whether and how media

reporting affects stock prices. This line of literature focuses on the informational effects of

media.4 For example, studies show that stock returns can be predicted by the tone of news

articles (Tetlock (2007), among others) and that of social media such as Twitter (Chen, Hwang,

and Liu (2013)). Focusing on the media effects in television, Kim and Meschke (2013) find

abnormal returns around CEOs interviews on CNBC and that these returns can also be

4 Two notable exceptions are Dougal, Engelberg, Garcia and Parsons (2011), who find that the style of journalists

affect stock returns, and Kim and Meschke (2013), who find that stock trading after CEO interviews on CNBC is

positively related to attractive anchorwoman and more male viewership.

6

explained by the tone of CEOs during the interview. Our findings suggest that factors unrelated

to informational contents, such as the attractiveness of interviewees on television, matter for

stock returns.

The rest of the paper is organized as follows. Section I reviews related literature and

develops the hypotheses. Section II describes the data and the construction of CEOs Facial

Attractiveness Index. Section III presents the main results. We report further robustness tests in

Section IV. Section V concludes.

I. Literature Review and Hypothesis Development

A. Literature Review

The effects of physical attractiveness have been the central issue of literature in sociology

and psychology. Studies along this line aim to address two main issues: First, do more attractive

people exhibit different characteristics (such as personality traits, skills and behavioral

tendencies) than unattractive people? Second, do more attractive people receive different

perceptions and treatments from others than unattractive people? In answering the first issue, a

plethora of experimental studies suggest that more attractive people show more socially desirable

personalities (Adams (1977), Langlois et al. (2000)), are better able to resist peer pressure

(Adams (1977)), are happier (Hamermesh and Abrevaya (2013)), more confident (Mobius and

Rosenblatt (2006)), more optimistic (Chaiken (1979)), and are more intelligent (Kanazawa

(2011)). As a result, more attractive individuals appear to be more effective communicators

(Chaiken (1976)) and negotiators (Rosenblatt (2008)); they receive a greater surplus in

negotiating games (Rosenblatt (2008)) and more fundraising success (Price (2008)), possibly due

7

to the acquisition of social skills developed through more positive attention from parents,

caregivers, teachers, and coworkers (Hatfield and Sprecher (1986), Langlois et al. (2000)).

In response to the second issue, Status Characteristics Theory (Berger et al. (1972)) posits

that perceptions and expectations of other people are based on observable characteristics, which

reflect status in our societyrace, age, sex, and attractiveness. Consistent with this theory, more

attractive people are perceived to have better abilities (Webster and Driskell (1983)), possess

greater social influence (Chaiken (1986)), are better recognized by peers (Kennedy (1990)), are

evaluated in more positive light, receive better treatment in a variety of settings (Hosoda et al.

(2003), Langlois et al. (2000)), and even are viewed as less disturbing when they are maladjusted

(Cash et al. (1977)).

Based on the literature summarized above, it is not surprising that the literature finds

more attractive people attaining better social and economic achievements, including better

academic performance (Jackson, Hunter, and Hodge (1995)), higher income (Hamermesh and

Biddle (1994)), and more favorable hiring decisions (Gilmore, Beehr, and Love (1986)). Further,

in the finance literature, Pareek and Zuckerman (2013) show that more trustworthy hedge fund

managers attract greater fund flows, are more likely to survive, but dont possess better skills.

Finally, attractive people also receive more advantages in personal finance. For example, Duarte,

Siegel, and Young (2012) and Ravina (2012) find that more trustworthy and/or beautiful

borrowers are more likely to secure their loans and pay lower interest rates.

Despite the aforementioned evidence on how appearance affects personal finance and

investments, few studies in finance investigate the relation between CEO appearance and

corporate outcomes; nor do they provide deterministic findings. In the seminal work, Graham,

Harvey and Puri (2010) find that more attractive CEOs receive higher compensation, but dont

8

seem to improve firm performance. In the psychology literature, by selecting 50 companies that

were listed in the Forbes 500 Companies in 2006, Rule and Ambady (2008) find a positive

correlation between CEO appearance and corporate profits. The present research contrasts with

these studies in the following aspects. First, by studying 682 CEOs from the S&P 500 firms

between 2000 and 2012, we conduct a large-sample analysis and thus provide more systematic

evidence on the effects of CEO appearance. Further, we perform robustness tests that mitigate

potential endogeneity concerns. Finally, we investigate the sources of the CEO appearance effect

and find that negotiating and visibility channels help explain these findings.

B. Hypothesis Development

The key question in this article is whether and how more attractive CEOs enhance

shareholder value. More attractive CEOs may contribute to shareholders in different ways that

reflect their more desirable personality traits, better abilities and perceptions. Accordingly, we

develop several hypotheses that form the basis for the empirical tests in the subsequent sections

of this article.

The first hypothesis relates to the existence of the value enhancing effects of CEO

appearance. Based on the aforementioned review of literature, more attractive people may have,

or are perceived to have, certain attributes and abilities that create value. Therefore, investors

may infer these attributes and abilities from a CEOs appearance and make investment decisions

accordingly. Further, Barberis, Mukherjee, and Wang (2013) show that stock returns can be

explained by investors first impressions. If CEO appearance is indeed factored into investors

assessment and thus affects shareholder value, a natural starting point to gauge this effect is to

examine the stock price reaction to the CEOs job announcement date, as many form their first

impressions of the CEO at that time. More formally:

9

Hypothesis 1 More attractive CEOs are associated with better stock returns around their

job announcement dates.

The above hypothesis discusses the existence of the value enhancing effect of CEO

appearance, if any. But a natural follow-up question is why CEO attractiveness is associated with

better shareholder value. The following two hypotheses aim to answer this question.

First, as suggested by the existing literature, attractive individuals show more socially

desirable qualities and receive better recognitions and advantages in social interactions; further,

they are indeed better communicators and negotiators. Therefore, more attractive CEOs may

show better negotiating prowess and enhance shareholder value through corporate events in

which interpersonal and negotiation skills are extremely important. Mergers and acquisitions

(M&As) provide an ideal setting for testing this negotiating channel, through which attractive

CEOs enhance shareholder value, for the following reasons. First, M&As are considered as

important and even milestone corporate events that significantly affect firm value. Second, large

M&As demand CEOs considerable involvement; CEOs interpersonal and negotiating skills are

documented to be an important factors in deciding the success of these deals. We therefore

hypothesize that more attractive CEOs create value for shareholders in M&As through the

negotiating channel:

Hypothesis 2 (Negotiating Channel): More attractive CEOs are associated with better

acquirer returns around the announcement of M&A transactions.

The second channel is related to the visibility of CEO attractiveness. The aforementioned

Status Characteristics Theory suggests that people are likely to form their perceptions and

expectations of an individual based on his or her attractiveness. Indeed, marketing literature

shows that more attractive celebrity endorsers are positively associated with consumers

10

purchase intentions (Kahle and Homer, (1985)) and that buyer satisfaction is positively

associated with the sellers attractiveness (Campbell, Graham, Jolibert and Meissner (1988)).

CEOs are often perceived as the embodiment of the firm and are indeed the principal corporate

decision makers. Therefore, more attractive CEOs are likely to create better images not only for

themselves but also for the firm, thus enhancing shareholder value. A natural testing ground for

this visibility channel is the study of stock price reactions on CEOs television appearances, as

Kim and Meschke (2013) find evidence of abnormal returns on the day of CEOs interviews. If,

indeed, visibility is an important channel for attractive CEOs to create positive images about the

firm and thus firm value, we should expect a positive relation between CEO appearance and

stock prices on days when the CEOs image appears on television. Therefore:

Hypothesis 3 (Visibility Channel): More attractive CEOs are associated with better stock

returns when the CEOs image appears on television.

II. Measure of CEO Attractiveness and Sample Description

In this article, we employ different samples to test the aforementioned hypotheses on

whether and how CEO appearance relates to shareholder value. In what follows, we first discuss

how we measure CEO attractiveness in Subsection A. Subsection B describes the sample used in

different tests, including (1) the main CEO sample with their Facial Attractiveness Index, as well

as samples used to study CEO attractiveness effects around (2) job announcement (3) M&A

announcement, and on (4) CEOs appearance on television news date. We finally present

descriptive statistics in Subsection C.

A. Measuring CEOs Facial Attractiveness

11

The effects of perceived facial attractiveness has been well studied in the psychology

literature (Cunningham (1986), Cunningham, Barbee, and Pike (1990), Cunningham et al. (1995),

Rhodes and Tremewan (1996), Rhodes et al. (1998), Rhodes, Sumich, and Byatt (1999)). A

majority of this literature measures facial attractiveness based on ratings given by survey

respondents. Recently, biostatisticians have started to use facial geometry calculated from

standard images to measure facial attractiveness. For example, using neoclassical cannons,

symmetry, and golden ratios, Schmid, Marx, and Samal (2008) take facial measurements from

different landmarks on the face and compute facial attractiveness scores accordingly.5 In this

paper, we calculate the Facial Attractive Index (FAI) of CEOs from anaface.com, which uses

similar techniques to those used by Schmid, Marx, and Samal (2008). The frequently Asked

Questions section on the website provides the following information regarding how it measures

facial geometry:

[Anaface.coms] specific algorithm is proprietary, but we take into account many factors

from neoclassical beauty, modern research papers, and our own scientific

studies/statistical analysis. Examples include things such as comparing innerocular

distance to mouth width and nose width to face height.

Anaface.com requires the user to upload a photograph to the website and place 17

different markers at different facial landmarks on the photograph (see Figure 1 for an example).

Anaface.com then scores each face based on its proprietary algorithm. As shown in Figure 1,

anaface.com also provides some guidance on which factors contribute to the overall score:

Horizontal symmetry, the ratio of nose to ear length, the ratio of eye width compared to

innerocular distance, the ratio of nose width to face width, the ratio of face width to face height,

5 We contacted one of the authors in this study for the use of their measure, which was not readily available for

distribution.

12

and the ratio of mouth width to nose width. We present an example on how we obtain the Facial

Attractive Index from Anaface.com using the stock photo provided by Anaface.com.

[Figure 1 goes here]

This geometry-based facial attractiveness measure provides the following advantages: (1)

this measure is based purely on facial geometry and excludes more subjective criteria such as eye

color, skin color, and complexion, thus avoiding potential selective perception bias commonly

seen in survey-based measures, and (2) this measure is easy to quantify using geometry and

mathematics.

A potential limitation of the measure from the anaface.com originates from the precision

requirements on the CEO photos. The uploaded CEOs photos need to have (1) sufficient

resolution, (2) the CEOs face is looking directly at the camera, and (3) each of the facial

landmarks required by anaface.com must be visible.6 We collect photographs for each of the 820

CEOs in our initial sample by conducting image searches on Google.com. We are able to

carefully select a single image for 682 of the 821 CEOs that satisfy the requirement of

anaface.coms algorithm. In what follows, we describe the samples and data source in detail.

B. Sample

B1. Main CEO Sample with Facial Attractiveness Index

The selection of our main sample begins with the intersection of the Execucomp Annual

Compensation file and the Compustat North America Fundamentals Annual file. Both data are

available on Wharton Research Data Services (WRDS). Because we rely on Google.com image

searches to compute the Facial Attractiveness Index of CEOs, we restrict the sample period to be

between 2000 and 2012 and only include firms that are in the S&P 500 index in Execucomp.

6 For example, one of the landmarks required by anaface.com is the top of the CEOs ears. This is especially

problematic for female CEOs with long hair styles.

13

These two screening procedures ensure that (1) the CEO in question is more likely to be a public

figure as he or she is leading a large public company, and (2) images of the CEO are more likely

to be available on the Internet following 2000 after its gained general popularity. These screens

result in 821 unique firm-CEO combinations. After eliminating observations with missing

firm/CEO level data or without CEOs photos, the final sample consists of 682 CEOs. We

further obtain these CEOs characteristics from Execucomp, including their age, gender, as well

as the starting and ending dates on the job. Finally, companies stock price information comes

from CRSP, and their accounting information comes from Compustat. Table I provides

definitions of variables used in this paper and their data sources.

[Table I goes here]

B2. Sample on CEOs Job Announcements

To analyze whether more attractive CEOs are associated with better stock returns around

the job announcement dates as predicted in Hypothesis 1, we base our sample on the 682 CEOs

with FAIs (described in subsection B1) and hand-collect data on their job announcement dates

from two sources: Lexis Nexis and Proquest. From both databases we search all online and print

articles related to the CEOs job announcements. In the vast majority of cases we are able to find

unique announcement dates; in cases where we find multiple report dates about a CEOs job

announcement, we select the earliest report dates as the announcement date. We further verify

the validity of these announcement dates using CEOs profiles in Bloomberg Businessweek

website and in Forbes.com. In addition, we exclude interim CEOs, cases where the CEOs job

announcement date is confounded by another major corporate event such as divestitures or

bankruptcies, and if the CEO is a founder. The final sample contains 489 job announcement

14

dates of 487 CEOs (out of 682 CEOs with Facial Attractiveness Index) between 1984 and 2012

from 289 firms.7 This sample is used in Table III.

B3. Sample on Mergers and Acquisitions

To test the Negotiating Hypothesis, in which more attractive CEOs are associated with

better acquirer returns around the M&A announcements, we rely on the acquirer information

provided by the Securities Data Company (SDC) Mergers and Acquisition Database. From this

database we identify all acquisition announcements that occurred during tenure of the 682 CEOs

in our main sample. We further exclude international acquisitions, acquisitions where the bidder

acquired less than 50% of the targets shares, and transactions for which we cannot compute the

ratio of transaction value to the bidders market value of equity (Transaction value). Finally, we

exclude acquisitions where the transaction value is less than $5 million or the ratio of the

transaction value to the bidders market value is less than 5% to ensure that we capture M&As

that are more likely to have a material impact on the firm and thus substantial CEO involvement.

The final sample contains 599 M&As from 1985 to 2012 that are associated with 278 CEOs (in

216 firms).8 This sample is used in Table IV.

B4. Sample on CEOs Television News Dates

The Visibility Hypothesis posits that more attractive CEOs enhance shareholder value

through their appearance on television. We identify television news events when the CEO or the

image of the CEO appears on television by conducting Internet searches using the video search

function from Google.com. We further restrict the search to only the news from CNBC.com. We

7 We find two CEO who switch firms within the sample. Therefore, we have 487 CEOs with 489 job announcement

dates. Further, our sample contains CEOs who held their positions during 2000 to 2012, but the job announcement

dates of CEOs started as early as 1984. 8 Our sample contains CEOs who held their positions during 2000 to 2012, but some CEOs in this sample started

their tenure as early as 1985. Since we trace all M&A transactions that occur during a CEOs tenure, we include

these transactions from 1985.

15

search for each CEO by name and record the headline and air date of each television news event.

The availability of CEO television news events on CNBC.com is limited prior to 2008, so we

restrict our sample to be between 2008 and 2012. We additionally require that each television

news event air during the CEOs tenure.

To mitigate the endogeneity concerns for analyses on the television news sample, we

form a matched sample of non-television news events as control group, i.e., news articles that

contain information on the same group of CEOs, but do not include any image of the CEOs. We

further restrict that these non-television news events occur within ten days before or after each

CEO television news event date. To identify the non-television news events that involve the

same group of CEOs, we search Proquests ABI/Inform Complete by CEO name and company.9

To ensure that our print news event is not a transcript from television news, we exclude news

articles that have the following keywords: CNBC, Bloomberg, CBS, Fox News,

MSNBC, CNN, ABC, NBC, TV, tv, or television in the headline, abstract,

copyright, or publication title. We further exclude print news articles with CEOs images. Finally,

to ensure that the effects of the visibility of CEOs attractiveness are not contaminated, we

exclude those television (print) news events that occur within +/- 1 day of the print (television)

news event. Our final sample of clean television (print) news events contain 891 (952)

observations. The samples of both television and print news events are used in Table V.

C. Summary Statistics

Panel A of Table II reports the summary statistics. The Average Facial Attractiveness

Index (FAI) of CEOs in the main sample is 7.26 (The maximum score is 10) and does not

materially change across samples of CEO job announcements, mergers announcements, and

9 Proquests ABI/Inform Complete is a comprehensive database of news stories including newspapers, magazines,

news wires, annual reports, and scholarly reports. We eliminate annual reports and scholarly reports from our

searches.

16

television/print news events (ranging from 7.21 to 7.33). For firm characteristics, the average

firm size as proxied by market value of equity ranges from $15.99 to 49.62 billion across

different samples, reflecting our sample selection criteria that focus on large U.S. public

companies.

It is possible that certain types of firms or firms in certain industries tend to select more

attractive CEOs, raising concerns that the effects of FAI on shareholder value are confounded by

other firm characteristics. For example, larger companies or companies with worse performing

firms may have more resources or incentives to hire more attractive CEOs. To mitigate this

concern, In Panel B (Table II) we report regressions results of FAI as the dependent variable on

the firm-specific control variables in each of the aforementioned samples. We dont find FAI to

be consistently correlated with these firm characteristics, including firm size, firm value (proxied

by firm market-to-book ratio), and stock returns. In unreported results, we do not find that the

FAI measure is correlated with industry. .

[Table II goes here]

III. Empirical Results

In Section I we develop three sets of hypotheses on the impact of CEO attractiveness. We

test these hypotheses in this section. We first analyze the effects of FAI on shareholder value

around CEOs job announcements in Subsection A. We next examine the CEO attractiveness

effects around M&As in Subsection B. In Subsection C we investigate how FAI affects stock

returns around CEOs appearance in television versus print news. Finally, in Subsection D we

examine the persistence of the CEO attractiveness effects.

A. CEO Attractiveness and Stock Returns around CEOs Job Announcement

17

Hypothesis 1 states that firms with more attractive CEOs receive better stock price

reactions around their job announcement dates. Table III presents regressions of cumulative

abnormal returns surrounding the announcement dates on FAI. To ensure the robustness of the

results, we analyze multiple event windows from event window (-1, 1) to event window (-5, 5).

Abnormal returns are calculated using the market-model estimated over 255 trading days, ending

46 trading days before the event date. FAI has a positive and significant impact on stock returns

surrounding the announcement dates. Moreover, this effect appears to be economically

significant: For the (-5, 5) event window, a ten percent increase in FAI results in a 1.37 %

increase in stock returns. These findings suggest that shareholders perceive more attractive CEOs

to be more valuable.

In Table III we further extend the analysis to longer event windows, starting from five

days before to 60 days after the announcement dates. FAI continues to have positive and

significant impact on stock returns. Further, the economic significance grows even larger with a

longer event window: A ten percent increase in FAI corresponds to a 2.08% increase in stock

returns for the event windows from five days before to 60 days after the announcement. We

further analyze persistence of the CEO attractiveness effect in Subsection D.

Finally, to ensure positive effects of FAI are not confounded by other factors, in Table III

we control for a number of firm and CEO characteristics variables that are documented to affect

announcement stock returns: Firm Size, market-to-book ratio, stock returns, leverage, whether

the CEO is female or not, and the age of the CEO. In addition, the nature of CEO turnover event

could also affect stock returns and thus confound the effects of FAI. For example, if the

succession of a new CEO is planned before the announcement, then the new CEOs FAI might

have been incorporated into stock prices before his or her job announcement. Similarly, the

18

effects of FAI could be confounded by whether the new CEO is selected within or outside the

company, whether the company in question hires a new CEO who used to be an executive from

another company (i.e., the new CEO is raided from another company), and when there exists a

period of power vacuum between the former CEOs departure and the new CEOs announcement

(called marathon succession). To control for these potential confounding effects related to CEO

turnovers, in all models of Table III we include four dummy variables, namely, Planned, Internal,

Raided, and Marathon. Controlling for these potential confounding variables does not affect the

significance of the FAI effects.

[Table III goes here]

B. CEO Attractiveness and Acquirer Returns

The results reported in III.A suggest that more attractive CEOs are associated with better

stock returns around job announcement dates, but they do not reveal why more attractive CEOs

enhance shareholder value. According to the Negotiating Hypothesis, if more attractive CEOs do

indeed have greater negotiating skills, we would expect more attractive CEOs to negotiate larger

surpluses from corporate events in which CEOs interpersonal and negotiating abilities are

extremely important, such as M&As. Consequently, we expect acquirers stock returns

surrounding M&A announcements to be positively correlated with FAI.

Table IV shows the regression results of abnormal acquirer returns surrounding the M&A

announcements on CEOs Facial Attractiveness Index. We also use multiple event windows to

assess the robustness of the results. As before, the abnormal returns are calculated using the

market-model estimated over 255 trading days while ending 46 trading days before the event

date. Consistent with the Negotiating Hypothesis, we find a positive relation between the

acquirers stock returns and the CEOs FAI surrounding the mergers announcement dates.

19

Specifically, a ten percent increase in FAI creates a 1.31% increase in acquirer returns for the

event window from five days before to five days after the merger announcement, and a 3.14%

increase when we expand event window from five days before to 60 days after announcement.

Again, we further examine the persistence of these effects in Subsection III.D.

The positive effects of FAI around M&A announcement could be confounded by other

firm characteristics variables. For example, Betton, Eckbo, and Thorburn (2008) find that firm

size and whether the target firm is publicly listed or not are two important determinants in

explaining acquirer returns in M&As. In Table IV We control for these variables and, consistent

with Betton, Eckbo, and Thorburn (2008), we find negative effects of these variables on acquirer

returns. More importantly, controlling for these factors does not affect the significance of FAI

effects. Further, since FAI is positively correlated with firm size as we reported in Panel B of

Table II, this positive relation may in fact induce a downward bias in our estimates of the effect

of FAI.

The positive effects of FAI could also be confounded by the anticipation that an M&A

deal will be announced or not. Specially, if the probability that the deal will be announced is

somehow correlated with FAI, this correlation may render unreliable estimates of the positive

FAI effects on acquirer returns. We address this concern by including Initial Bid, a proxy for the

anticipation of merger announcements, in the regressions reported in Table IV (Cai, Song, and

Walkling (2011)). Initial Bid is a binary variable that takes the value of 1 if no other bids have

occurred in the bidders industry over the prior 365 days. We find that controlling for initial bids

in our regression analysis does not alter positive FAI effects on acquirer returns.

Finally, in Table III and IV, the positive and significant relations between FAI and stock

returns around CEOs job and mergers announcements could be driven by additional

20

characteristics that we do not include in the regressions due to data availability issues.10

Therefore, though we try to address the potential selection bias in the robustness checks section

(in Section IV), we dont claim to find a causal relation, but a positive correlation, between FAI

and shareholder value in these two tests. We aim to mitigate the endogeneity concerns and build

a stronger causal link between CEO attractive and shareholder value in our next test in

Subsection C.

[Table IV goes here]

C. CEO Attractiveness and Stock Returns around News Events

This section explores whether more attractive CEOs improve shareholder value through

public appearances (The Visibility Hypothesis). Therefore, the first test in this section examines

whether CEO attractiveness positively affects the stock returns around television news with his

or her presence or image. We acknowledge that this test may also be plagued by the typical

endogeneity problem. For example, the visibility of more attractive CEOs might be correlated

with unobservable variables that are also correlated with stock returns. In this case, the

interpretation of the results that the visibility of more attractive CEOs causes a higher

shareholder value is misleading. We undertake the following analysis to address this concern.

We first form a matched sample of non-television news events, i.e., news articles that

contain information on the same group of CEOs, but do not contain any image of the CEOs. By

comparing the effect of CEO attractiveness on stock returns surrounding the television news

events to those around matched non-television event dates, we aim to net out any potential

unobservable firm and CEO characteristics that could otherwise confound the effects of

attractiveness. In addition, if the visibility of more attractive CEOs enhances shareholder value

10

These characteristics include CEOs IQ, other physical attributes such as height and BMI as well as additional

unobservable CEO and firm characteristics.

21

through the visibility channel, we would expect FAI to have an insignificant effect on stock

returns around the matched non-television news days.

Table V presents the OLS regressions of abnormal stock returns surrounding the news

announcements on Log(FAI) for multiple event windows. The relation between stock returns and

Log(FAI) on television news days is positive and statistically significant, while the relation

between stock returns and Log(FAI) on print news days is insignificant. Further, the coefficient

on Log(FAI) in the television news regression is 13.5 times larger in magnitude than the

coefficient on Log(FAI) in the print news regression.11

We also examine the effects of CEO attractiveness on television news days using longer

event windows in Table V. FAI only has positive and significant impacts on stock returns on

event windows (-1, 1) and (-2, 2).

Finally, to ensure that our facial attractiveness measure does not represent an

unobservable, time-invariant factor that may drive the above result, in Table V we also

investigate the effect of Log(FAI) on the abnormal returns in the (-1,-1) event window. FAI does

not significantly affect the stock returns on the date before both the television and print news

events, thus greatly reducing the possibility that the CEO attractiveness might proxy for some

unobservable factor(s). Overall, the evidence suggests that shareholders respond positively to

viewing more attractive CEOs, consistent with more attractive CEOs improving shareholder

value through the visibility channel.

[Table V goes here]

D. Long-run Effects of CEO Attractiveness

11

We use a Wald test of the significance of the difference between the marginal effect of FAI on television and print

news days.

22

The findings thus far reveal positive and significant relation between CEO attractiveness

on shareholder value around their job announcement, acquirer M&A announcement, and

television appearances. These results raise a follow-up question: How persistent are these CEO

attractiveness effects? Results reported in Table III and Table IV show that FAI continues to

significantly impact abnormal returns up to 60 days after the CEOs job announcement and

M&A announcement. In this section we further investigate whether the CEO attractiveness

effects persist for even longer periods.12

We separate the sample firms into two portfolioshigh

and low FAI portfoliosbased on a median split of CEOs FAIs. We then compare the post-

event long-run stock performance of each portfolio.

Table VI reports the results. We calculate two measures of long-run stock returns for

each portfolio: Buy-and-hold abnormal returns relative to size and book-to-market benchmark

portfolios, and monthly (calendar-time) factor-adjusted abnormal returns. These factors include

the three factors introduced by Fama and French (1992) and the momentum factor introduced by

Carhart (1997). We find no significant difference in abnormal returns between the high and low

FAI portfolio following the CEOs job announcement. On the other hand, following the M&A

announcements, firms in the high FAI portfolio outperform those in the low FAI portfolio by

4.7% in buy-and-hold abnormal returns in the six-month window, and up to 15.5% in the two-

year window. This difference is statistically significant at the 10% level or lower. In terms of

calendar-time abnormal returns, the high FAI portfolio outperforms the low FAI group by 1% in

monthly alphas (or 12% in annual abnormal returns) in the six months following the M&A

announcements, and by 0.7% (or 8.4% in annual abnormal returns) within twelve months.

12

We do not report the long-run effects of CEO attractiveness following their television news events as the media

effects on stock prices through television tend to be transitory. Kim and Meschke (2013) find positive abnormal

returns around the day of CEOs interviews on CNBC, but prices experience strong reversal in the following ten

days. In Table V, FAI fails to significantly impact stock returns more than two days after CEOs television

appearances, consistent with the transitory nature of television media effects.

23

Overall, the findings seem to suggest that more attractive CEOs create greater long-term values

for shareholders through better negotiating prowess in M&A deals.

[Table VI goes here]

IV. Robustness Checks

A. Tests for Selection Bias in the Absence of Control Samples

Sections III.A and III.B provide evidence that CEO appearance is positively associated

with stock returns in the context of CEOs job and M&A announcements. However, these events

are firms voluntary choices and lack observable control samples. Therefore, it is likely that

these events are initiated when firms possess information not fully known to markets, and that

the unobservable factors that determine the decisions to replace a CEO and/or undertake an

M&A are also correlated with FAI. Consequently, the OLS model used in the prior analysis to

estimate the relation between announcement returns and FAI may result in a potential selection

bias (Prabhala (1997)). To address this concern, we follow Eckbo, Maksimovic, and Williams

(1990) and estimate the following conditional model using maximum likelihood estimation

(MLE) for both the CEO job announcements and M&A announcements tests we report in Table

III and IV:

[ |

( )

( ) (1)

where is the cumulative abnormal return; is the vector of regressors for manager i,

including FAI; is the vector of coefficients; is assumed to be normally distributed with

variance ; and represent the normal probability density function and cumulative density

function, respectively.

24

Intuitively, (1) accounts for private information that is related to the decision to replace a

CEO and the acquisition announcement. In an unreported analysis (available upon request), we

find that, after addressing this selection bias in the absence of control events, the coefficients of

FAI estimated from (1) remain positive and significant; further, both the economic and statistical

significance does not materially change from that reported in the OLS regressions in Tables III

and IV. These findings further support our main findings.

B. Tests Controlling for Selection Bias in News Events

In our test of the Visibility Hypothesis (reported in Section III.C), we aim to control for

potential endogeneity issues due to confounding unobservable firm and CEO characteristics that

may be driving our results; Specifically, we compare the difference in the effect of FAI on stock

returns between CEOs television news events verses a matched sample of print news events.

Doing so enables us to net out any potential firm and CEO unobservable characteristics.13

However, this approach does not control for the potential selection bias that FAI is correlated

with unobservable factors that determine whether a CEO is selected into television or print news

events.

To mitigate this potential selection bias, we re-estimate the effects of FAI on stock

returns during television and print news events using Heckmans (1979) procedure. We estimate

outcome and selection models by MLE. In the selection equation, we consider major sports

event days as a source of exogenous variation.14

We hypothesis that CEOs are more likely to

appear on CNBC in the days leading up to major sporting events than to appear in print news

13

In this test, we assume that these unobservable firm and CEO characteristics remain time invariant within ten days

surrounding these events. 14

We include the following major sporting events: National Football Leagues (NFL) playoffs, Super Bowl,

National Basketball Associations (NBA) playoffs and finals, National Hockey Leagues (NHL) playoffs and finals,

American League Championship Series (ALCS), National League Championship Series (NLCS), World Series

(WS), the 2008 and 2012 Summer Olympics, and the 2010 Winter Olympics. If an event occurs on a non-trading

day we assign it to the first trading day prior to the event.

25

events. This is because, for the majority of print news outlets, the business news sections are

more likely to compete for space and prominence with sports news sections during major sports

events. In contrast, business television stations such as CNBC are less likely to divert to sports

news as NBC has its own sports channels. Further, it is unlikely that an individual firms stock

returns are driven by contemporaneous sports news events. Consistent with this conjecture, in

unreported results (available upon request), we find that the CEO is less likely to be selected into

print news days on the major sports events days.15

[Table VII goes here]

Table VII reports the results for the estimated outcome equation of abnormal returns on

television and print news days on FAI, other controls. The coefficient on the IMR is significant

in both regressions at the 5% level, indicating that investors expectations and selection into

television and print news are jointly determined. More importantly, the coefficient on FAI

remains significant and positive at the 5% level on CEO television news days, and its magnitude

is nearly double the coefficient reported in the OLS regressions in Table V. Further, the

coefficient on FAI remains insignificant on print news days. We further perform a test on the

difference between the coefficients on FAI in the television and in print news events, and find

that the difference is significant at the 5% level. Overall the results further support a positive

effect of FAI on shareholder value through the visibility channel.

V. Conclusion

15

The coefficients are significant at 10% level during Olympic Games and National Hockey League (NHL) finals

days. In addition, we include all other control variables used in the outcome equation in the selection equation

except firm and industry fixed effects.

26

In this paper, we investigate whether and how CEO appearance matters for shareholder

value. We calculate the Facial Attractiveness Index of CEOs based on their facial geometry. We

first document the existence of the CEO appearance effects on shareholder value by showing that

more attractive CEOs are associated with better stock returns around their job announcement

dates. We further hypothesize and test two channels through which more attractive CEOs

enhance shareholder value: negotiating and visibility. To test the negotiating channel, we

examine the stock price reaction around M&A announcement dates and find a positive and

significant CEO attractiveness effect on acquirer returns. This positive relation persists after one

year following the mergers announcement dates. We test the visibility channel by investigating

the stock price reaction around CEO television news event dates and find that more attractive

CEOs are associated with better stock returns around CEO-related television news days.

However, we find no significant relation between CEO attractiveness and stock returns around a

matched sample of non-television news events. This result mitigates endogeneity concerns when

interpreting our findings. Overall, our findings suggest that more attractive CEOs receive higher

compensation because they create more value for shareholders through better negotiating

prowess and visibility.

The findings of this paper shed light on how the appearance of corporate insiders affects

corporate decisions and outcomes. It is well established in the asset pricing literature that

investors decisions are likely based on initial, possibly unconscious, impressions and

perceptions. Along this line, several studies find evidence of how a first impression effect of

appearance impacts personal financing. However, less is known about how the first impression

effect of appearance of corporate insiders would affect the perceptions and thus decisions of

corporate stakeholders. More research is called for to further assess these possibilities.

27

Appendix. CEO Compensation and FAI

We posit that, in a competitive labor market, given the evidence that better looking CEOs

receive higher pay (beauty premium), we expect that more attractive CEOs contribute to

shareholder value. This article shows that more attractive CEOs are associated with better

shareholder values using the Facial Attractiveness Index (FAI) of CEOs, but we does not report

that CEOs with higher FAIs receive higher pay. To complete the above argument and be in line

with existing literature, such as Graham, Harvey, and Puri (2010), who find a positive relation

between CEO attractiveness and compensation, in this appendix we examine the relation

between FAI and CEO pay using panel regressions. The regressions include firm fixed-effects to

control for firm specific time-invariant factor(s). From Table A.I, we find a positive relation

between FAI and Total compensation, indicating that more attractive CEOs receive higher

annual total compensation. This result is robust to different models using the natural logarithm of

FAI as the main explanatory variable and/or using the natural logarithm of 1+Total compensation

as the dependent variable.

28

References

Adams, G.R., 1977. Physical Attractiveness, Personality, and Social Reactions to Peer Pressure.

The Journal of Psychology 96, 287296. doi:10.1080/00223980.1977.9915911

Addoum, J.M., Korniotis, G.M., Kumar, A., 2013. Stature, Obesity, and Portfolio Choice (SSRN

Scholarly Paper No. ID 1509173). Social Science Research Network, Rochester, NY.

Alicke, M.D., Smith, R.H., Klotz, M.L., 1986. Judgments of Physical Attractiveness The Role of

Faces and Bodies. Personality and Social Psychology Bulletin 12, 381389.

Barberis, Nicholas., Abhiroop. Mukherjee, and Baolian. Wang.2013). First Impressions:

System 1 Thinking and the Cross-section of Stock Returns. Working Paper, Yale

University.

Berger, Joseph, Bernard P. Cohen, and Morris Zelditch Jr. 1972. Status Characteristics and

Social Interaction. American Sociological Review 37 (3) (June 1): 241255.

Berscheid, E., Dion, K., Walster, E., Walster, G.W., 1971. Physical attractiveness and dating

choice: A test of the matching hypothesis. Journal of Experimental Social Psychology 7,

173189.

Betton, S., Eckbo, B. E., Thorburn, K. S., 2008. Corporate takeovers. SSRN Scholarly

PaperUnpublished working paper, Social Science Research Network.

Cai, J., Song, M. H., Walkling, R. A., 2011. Anticipation, acquisitions, and bidder returns:

industry shocks and the transfer of information across rivals. Review of Financial Studies

24, 2242 2285.

Cann, A., Siegfried, W.D., Pearce, L., 1981. Forced attention to specific applicant qualifications:

Impact on physical attractiveness and sex of applicant biases. Personnel Psychology 34,

6575.

29

Carhart, M. M., 1997. On persistence in mutual fund performance. The Journal of Finance 52,

5782.

Cash, T.F., Kehr, J.A., Polyson, J., Freeman, V., 1977. Role of physical attractiveness in peer

attribution of psychological disturbance. Journal of Consulting and Clinical Psychology

45, 987.

Chen, Hailiang, Byoung-Hyoun Hwang, and Baixiao Liu. 2013. The Economic Consequences

of Having Social Executives. SSRN Scholarly Paper ID 2318094. Rochester, NY:

Social Science Research Network. http://papers.ssrn.com/abstract=2318094.

Chaiken, S., 1979. Communicator physical attractiveness and persuasion. Journal of Personality

and social Psychology 37, 1387.

Chaiken, S., 1986. Physical appearance and social influence, in: Physical Appearance, Stigma,

and Social Behavior: The Ontario Symposium. pp. 143177.

Clifford, M.M., 1975. Physical attractiveness and academic performance. Child Study Journal.

Cunningham, Michael R., 1986, Measuring the physical in physical attractiveness: quasi-

experiments on the sociobiology of female facial beauty. Journal of Personality and

Social Psychology 50, 925.

Cunningham, Michael R., Anita P. Barbee, and Carolyn L. Pike, 1990, What do women want?

Facialmetric assessment of multiple motives in the perception of male facial physical

attractiveness. Journal of Personality and Social Psychology 59, 61.

Cunningham, Michael R., Alan R. Roberts, Anita P. Barbee, Perri B. Druen, and Cheng-Huan

Wu, 1995, Their ideas of beauty are, on the whole, the same as ours: Consistency and

variability in the cross-cultural perception of female physical attractiveness. Journal of

Personality and Social Psychology 68, 261.

30

DeBruine, Lisa M. 2002. Facial Resemblance Enhances Trust. Proceedings of the Royal

Society of London. Series B: Biological Sciences 269 (1498) (July 7): 13071312.

doi:10.1098/rspb.2002.2034.

Dipboye, R.L., Arvey, R.D., Terpstra, D.E., 1977. Sex and physical attractiveness of raters and

applicants as determinants of resum evaluations. Journal of Applied Psychology 62, 288.

Dougal, Casey, Joseph Engelberg, Diego Garca, and Christopher A. Parsons. 2012. Journalists

and the Stock Market. Review of Financial Studies 25 (3) (March 1): 639679.

doi:10.1093/rfs/hhr133.

Duarte, Jefferson, Stephan Siegel, and Lance Young. 2012. Trust and Credit: The Role of

Appearance in Peer-to-Peer Lending. Review of Financial Studies 25 (8): 24552484.

Eckbo, B. E., Maksimovic, V., Williams, J., 1990. Consistent estimation of cross-sectional

models in event studies. Review of Financial Studies 3, 343365.

Faccio, Mara, Maria-Teresa Marchica, and Roberto Mura. 2012. CEO Gender, Corporate Risk-

Taking, and the Efficiency of Capital Allocation. SSRN Scholarly Paper ID 2021136.

Rochester, NY: Social Science Research Network.

http://papers.ssrn.com/abstract=2021136.

Fama, E. F., French, K. R., 1992. The cross-section of expected stock returns. The Journal of

Finance 47, 427465.

Gilmore, D.C., Beehr, T.A., Love, K.G., 1986. Effects of applicant sex, applicant physical

attractiveness, type of rater and type of job on interview decisions*. Journal of

Occupational Psychology 59, 103109.

Graham, John R., Campbell R. Harvey, and Manju Puri. 2010. A Corporate Beauty Contest.

http://faculty.fuqua.duke.edu/~charvey/Research/Working_Papers/W101_A_corporate_b

http://papers.ssrn.com/abstract=2021136

31

eauty.pdf.

. 2013. Managerial Attitudes and Corporate Actions. Journal of Financial Economics

109 (1) (July): 103121. doi:10.1016/j.jfineco.2013.01.010.

Hamermesh, Daniel S., and Jeff E. Biddle. 1994. Beauty and the Labor Market. The American

Economic Review 84 (5): 11741194.

Hamermesh, Daniel S., and Jason Abrevaya. 2013. Beauty is the Source of Happiness? The

European Economic Review 64: 351368.

Han, E., Norton, E.C., Stearns, S.C., 2009. Weight and wages: fat versus lean paychecks. Health

Economics 18, 535548. doi:10.1002/hec.1386

Hatfield, E. and S. Sprecher. 1986. Mirror, mirror:The importance of looks in everyday life.

Albany, NY: SUNY Press.

Heckman, J. J., 1979. Sample selection bias as a specification error. Econometrica 47, 153161.

Hosoda, Megumi, Eugene F. Stone-Romero, and Gwen Coats. 2003. The Effects of Physical

Attractiveness on Job-related Outcome: a Meta-analysis of Experimental Studies.

Personnel Psychology 56 (2): 431462.

Jackson, L.A., Hunter, J.E., Hodge, C.N., 1995. Physical attractiveness and intellectual

competence: A meta-analytic review. Social psychology quarterly 58, 108108.

Judge, T.A., Hurst, C., Simon, L.S., 2009. Does it pay to be smart, attractive, or confident (or all

three)? Relationships among general mental ability, physical attractiveness, core self-

evaluations, and income. Journal of Applied Psychology 94, 742.

Kanazawa, S., 2011. Intelligence and physical attractiveness. Intelligence 39, 714.

Kaplan, Steven N., Mark M. Klebanov, and Morten Sorensen. 2012. Which CEO

Characteristics and Abilities Matter? Journal of Finance 67 (3) (June): 9731007.

32

doi:10.1111/j.1540-6261.2012.01739.x.

Kennedy, Janice H. 1990. Determinants of Peer Social Status: Contributions of Physical

Appearance, Reputation, and Behavior. Journal of Youth and Adolescence 19 (3) (June

1): 233244. doi:10.1007/BF01537889.

Kim, Y. Han (Andy), and Felix Meschke. 2011. CEO Interviews on CNBC. SSRN Scholarly

Paper ID 1745085. Rochester, NY: Social Science Research Network.

http://papers.ssrn.com/abstract=1745085.

Korniotis, G.M., Kumar, A., 2011. Do Portfolio Distortions Reflect Superior Information or

Psychological Biases? (SSRN Scholarly Paper No. ID 1018668). Social Science Research

Network, Rochester, NY.

Landy, D., Sigall, H., 1974. Beauty is talent: Task evaluation as a function of the performers

physical attractiveness. Journal of Personality and Social Psychology 29, 299.

Langlois, J., L. Kalakanis, A. Rubenstein, A. Larson, M. Hallam, and M. Smoot. 2000. Maxims

of myths of beauty? A meta-analytic and theoretical review. Psychological Bulletin

126(3): 390423.

Malmendier, U., and G. Tate. 2005. CEO Overconfidence and Corporate Investment. Journal

of Finance 60 (6) (December): 26612700. doi:10.1111/j.1540-6261.2005.00813.x.

Malmendier, Ulrike, and Geoffrey Tate. 2008. Who Makes Acquisitions? CEO Overconfidence

and the Markets Reaction. Journal of Financial Economics 89 (1) (July): 2043.

doi:10.1016/j.jfineco.2007.07.002.

Malmendier, Ulrike, Geoffrey Tate, and Jon Yan. 2011. Overconfidence and Early-Life

Experiences: The Effect of Managerial Traits on Corporate Financial Policies. Journal

of Finance 66 (5) (October): 16871733. doi:10.1111/j.1540-6261.2011.01685.x.

33

Mazur, Allan, Julie Mazur, and Caroline Keating. 1984. Military Rank Attainment of a West

Point Class: Effects of Cadets Physical Features. American Journal of Sociology: 125

150.

Merton, Robert C. 1987. A Simple Model of Capital Market Equilibrium with Incomplete

Information. The Journal of Finance 42 (3): 483510. doi:10.1111/j.1540-

6261.1987.tb04565.x.

Mobius, M.M., Rosenblat, T.S., 2006. Why beauty matters. The American Economic Review

222235.

Naumann, Laura P., Simine Vazire, Peter J. Rentfrow, and Samuel D. Gosling. 2009.

Personality Judgments Based on Physical Appearance. Personality and Social

Psychology Bulletin 35 (12) (December 1): 16611671. doi:10.1177/0146167209346309.

Pareek, Ankur, and Roy Zuckerman. 2011. Trust and Investment Management: The Effects of

Manager Trustworthiness on Hedge Fund Investments. In AFA 2012 Chicago Meetings

Paper. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1659189.

Persico, N., Postlewaite, A., Silverman, D., 2004. The Effect of Adolescent Experience on Labor

Market Outcomes: The Case of Height. Journal of Political Economy 112, 10191053.

Prabhala, N. R., 1997. Conditional methods in event studies and an equilibrium justification for

standard event-study procedures. Review of Financial Studies 10, 138.

Price, M.K., 2008. Fund-raising success and a solicitors beauty capital: Do blondes raise more

funds? Economics Letters 100, 351354. doi:10.1016/j.econlet.2008.02.028

Puri, M., Robinson, D.T., 2007. Optimism and economic choice. Journal of Financial Economics

86, 7199. doi:10.1016/j.jfineco.2006.09.003

Ravina, Enrichetta. 2012. Love & Loans: The Effect of Beauty and Personal Characteristics in

34

Credit Markets. SSRN Scholarly Paper ID 1107307. Rochester, NY: Social Science

Research Network. http://papers.ssrn.com/abstract=1107307.

Rhodes, Gillian, and Tanya Tremewan, 1996, Averageness, exaggeration, and facial

attractiveness. Psychological Science, 105110.

Rhodes, Gillian, Fiona Proffitt, Jonathon M. Grady, and Alex Sumich, 1998, Facial symmetry

and the perception of beauty. Psychonomic Bulletin & Review 5, 659669.

Rhodes, Gillian, Alex Sumich, and Graham Byatt, 1999, Are Average Facial Configurations

Attractive Only Because of Their Symmetry? Psychological Science 10, 5258.

Rhodes, Gillian. 2006. The Evolutionary Psychology of Facial Beauty. Annu. Rev. Psychol. 57:

199226.

Rosenblat, Tanya S. 2008. The Beauty Premium: Physical Attractiveness and Gender in

Dictator Games. Negotiation Journal 24 (4): 465481. doi:10.1111/j.1571-

9979.2008.00198.x.

Rule, Nicholas O., and Nalini Ambady. 2008. The Face of Success Inferences From Chief

Executive Officers Appearance Predict Company Profits. Psychological Science 19 (2):

109111.

Schmid, Kendra, David Marx, and Ashok Samal, 2008, Computation of a face attractiveness

index based on neoclassical canons, symmetry, and golden ratios. Pattern Recognition

41 8: 27102717.

Tetlock, Paul C. 2007. Giving Content to Investor Sentiment: The Role of Media in the Stock

Market. The Journal of Finance 62 (3): 11391168.

Todorov, Alexander, Anesu N. Mandisodza, Amir Goren, and Crystal C. Hall. 2005. Inferences

of Competence from Faces Predict Election Outcomes. Science 308 (5728): 16231626.

http://papers.ssrn.com/abstract=1107307

35

Umberson, Debra, and Michael Hughes. 1987a. The Impact of Physical Attractiveness on

Achievement and Psychological Well-Being. Social Psychology Quarterly: 227236.

Webster Jr, M., Driskell Jr, J.E., 1978. Status generalization: A review and some new data.

American Sociological Review 220236.

36

Figure 1

This figure presents a screen shot of anaface.com. The photograph is the default image provided

by anaface.com.

37

Table I. Variable definitions

Table I reports the main variables used in the paper. The first column presents the variable name

we use throughout the paper. The second column provides a brief description of the variable,

along with any Compustat, Execucomp, or CRSP data items used to construct the variable. The

final column reports the data source(s) used to compute the variable. SDC in the final column

represents the Securities Data Corporation.

Variable Description Source

FAI Facial attractiveness index; the measure of a CEO's facial

geometry.

anaface.com

Size Market value of equity (in millions; CRSP Variable:

ABS(PRC*SHROUT)/1000).

CRSP

Leverage The ratio of total liabilities (Compustat Variable: LT) and total

assets (Compustat Variable: AT).

Compustat

MTB Market value equity to book value of equity (Compustat Variable:

CSHO*PRCC_F/(AT-LT)).

Compustat

Stock Return The firm's annual stock return measured over the past year. CRSP

Female A binary variable equal to one if the CEO is female (Execucomp

Variable: GENDER).

Execucomp

Age The age of the CEO in years (Execucomp Variable: AGE). Execucomp

Planned Indicator variable equal to one if CEO departs for reasons other

than the following: sudden death, illness, family reasons, or the

news article states the turnover was unexpected.

Proquest

LexisNexis

Forced Indicator variable equal to one if the CEOs predecessor was

forced out (e.g., the news article states the CEO was forced out or

the CEO was under 60 years old and did not take another position

either inside or outside the company).

Proquest

LexisNexis

Internal Indicator variable equal to one if the CEO was an employee of the

company for at least one year prior to the appointment of CEO.

Proquest

LexisNexis

Raided Indicator variable equal to one if the CEO was an employee of

another public company within one year of appointment as CEO.

Proquest

LexisNexis

Marathon Indicator variable equal to one if the CEOs predecessors

retirement date was announced before the current CEOs job

announcement date or the CEOs predecessor was an interim

CEO.

Proquest

LexisNexis

38

Transaction

value

The ratio of the transaction value to the bidders market value of

equity.

SDC

CRSP

Public An indicator variable if the target has a ticker on SDC or the

target is in the Compustat file in the year prior to acquisition.

SDC

Compustat

Initial bid An indicator variable equal to one if there has been no other bids

in the bidders industry for the past year.

SDC

39

Table II. Summary statistics

Table II presents summary statistics for each of the samples used in the paper. Panel A provides

summary statistics for the main sample of the 682 CEO observations as well as samples we use

in testing each hypothesis. Panel B presents OLS regressions of FAI on firm and event

characteristics for each sample.

Panel A. Descriptive Statistics

(1) (2) (3) (4) (5)

Sample Main Sample CEO Job

Announcement M&A

TV News

Events

News Article

Events

Sample period 2000 to 2012 1984 to 2012 1985 to 2012 2008 to 2012 2008 to 2012

Mean Median Mean Median Mean Median Mean Median Mean Median

FAI 7.257 7.340 7.281 7.350 7.224 7.300 7.210 7.040 7.328 7.490

CAR (-1, +1)

0.004 0.003 -0.004 -0.004 0.001 0.000 0.001 0.001

Size ($Mil) 26502 8442 25378 8421 15985 5180 41132 17057 49619 24947

MTB 4.660 3.241 4.692 3.033 3.975 2.556 2.968 2.496 3.627 2.946

Stock return 0.202 0.108 0.117 0.066 0.312 0.211 0.175 0.123 0.134 0.099

Leverage 0.554 0.559 0.562 0.566 0.542 0.553 0.573 0.571 0.574 0.596

Female 0.025 0.000 0.033 0 0.018 0 0.036 0 0.064 0

Age 54 53 51 51 53 53 56 54 54 54

Planned 0.908 1

Forced 0.086 0

Internal 0.771 1

Raided 0.211 0

Marathon 0.117 0

Transaction

value 0.332 0.126

Public

0.489 0

Initial bid 0.826 1

N 682 489 599 891 952

N(firms) 363 289 216 172 121

N(CEOs) 682 487 278 182 124

40

Panel B. Determinants of FAI

(1) (2) (3) (4) (5)

Full Sample CEO

Turnover M&A TV Print

Dep. Variable Log(FAI) Log(FAI) Log(FAI) Log(FAI) Log(FAI)

Log(Size) 0.018 -0.001 0.010** 0.007 0.012

(1.288) (-0.303) (2.357) (0.916) (0.870)

MTB 0.000 0.001 -0.001 0.000 0.002***

(0.117) (0.920) (-0.733) (0.533) (3.052)

Stock return -0.003 -0.011 0.010 0.001 -0.015

(-0.147) (-0.892) (1.405) (0.082) (-1.267)

Leverage 0.092 -0.004 0.019 -0.126** -0.192***

(1.020) (-0.148) (0.519) (-2.448) (-2.997)

Female -0.020 0.007 -0.027 0.074* -0.032

(-0.428) (0.178) (-0.574) (1.819) (-0.685)

Age -0.001 -0.001 -0.001 0.002 0.005**

(-0.732) (-0.648) (-0.606) (1.146) (2.161)

Planned -0.014

(-0.652)

Forced -0.004

(-0.192)

Internal 0.036

(0.802)

Raided 0.026

(0.591)

Marathon -0.004

(-0.270)

Transaction value 0.007***

(2.889)

Public 0.010

41

(0.904)

Initial bid 0.005

(0.251)

Intercept 1.959*** 1.966*** 1.927*** 1.893*** 1.687***

(10.817) (21.049) (36.119) (17.473) (10.230)

Industry controls Yes Yes Yes Yes Yes

Year controls Yes Yes Yes Yes Yes

SE clustered (CEO) No No Yes Yes Yes

SE clustered (firm) Yes Yes No No No

N 682 489 599 891 952

R-squared 0.551 0.424 0.288 0.480 0.522

42

Table III. Stock Price Reactions around CEOs Job Announcements

Table III presents regression analysis of cumulative abnormal returns (relative to the market-

model) surrounding the CEOs job announcements on the natural logarithm of FAI (Log (FAI)).

Various event windows (-day(s), +day(s)) are reported. Industry is defined as the 2-digit SIC

codes. Standard errors are robust to heteroskedasticity and within firm correlation (clustered

standard errors); t-statistics are reported in the parenthesis where ***, **, and * signify statistical

significance at the 1%, 5%, and 10% levels. The control variables are described in Table I.

Dependent Variable: CAR

(1) (2) (3) (4) (5) (6) (7) (8)

Event window (days)

(-1,-1) (0,0) (-1,1) (-2,-2) (-3,3) (-5,5) (-5,30) (-5,60)

Log (FAI) -0.004 0.031* 0.048* 0.095*** 0.118*** 0.137*** 0.136** 0.208***

(-0.325) (1.678) (1.898) (2.785) (3.270) (3.369) (2.194) (2.605)

Log(Size) -0.001 0.003** 0.001 -0.001 -0.002 -0.004 -0.012* -0.021***

(-0.725) (2.175) (0.891) (-0.537) (-0.593) (-1.310) (-1.958) (-2.975)

MTB 0.000*** -0.000 -0.000 -0.000 -0.000 -0.001 -0.001* -0.002

(2.616) (-1.398) (-0.330) (-0.259) (-1.227) (-1.327) (-1.921) (-1.440)

Stock return -0.003 -0.009* -0.015*** -0.021** -0.025** -0.034** -0.101*** -0.147***

(-1.138) (-1.776) (-2.746) (-2.172) (-2.295) (-2.441) (-3.936) (-4.742)

Leverage -0.008 0.002 -0.014 -0.011 -0.008 0.001 0.032 0.118*

(-1.013) (0.182) (-1.016) (-0.686) (-0.460) (0.056) (0.859) (1.820)

Female 0.010 -0.007 0.004 0.004 -0.002 0.001 0.039 0.012

(1.226) (-0.798) (0.263) (0.268) (-0.106) (0.058) (1.505) (0.268)

Age -0.000 0.000 0.000 0.000 0.001 0.001 -0.000 -0.001

(-0.796) (0.942) (0.808) (0.298) (0.756) (0.847) (-0.100) (-0.738)

Planned -0.015** -0.029* -0.035 -0.029 -0.041 -0.052** -0.076*** -0.041

(-2.298) (-1.689) (-1.616) (-1.211) (-1.502) (-1.984) (-2.783) (-1.163)

Forced 0.005 0.005 0.002 0.006 0.008 0.017 -0.004 0.002

(1.016) (0.716) (0.152) (0.582) (0.656) (1.116) (-0.235) (0.090)

Internal 0.000 -0.015* -0.004 -0.004 -0.010 -0.005 -0.023 0.008

(0.024) (-1.734) (-0.299) (-0.336) (-0.763) (-0.322) (-0.962) (0.218)

43

Raided -0.019*** -0.010 -0.011 -0.008 -0.017 -0.025 -0.040 -0.011

(-3.061) (-0.557) (-0.484) (-0.296) (-0.592) (-0.903) (-1.362) (-0.277)

Marathon 0.012* -0.008 0.002 0.007 -0.001 0.007 -0.023 -0.020

(1.897) (-1.089) (0.234) (0.550) (-0.107) (0.485) (-0.945) (-0.634)

Intercept 0.042 -0.074* -0.078 -0.147* -0.197** -0.220** -0.072 -0.175

(1.330) (-1.676) (-1.252) (-1.910) (-2.551) (-2.424) (-0.485) (-0.831)

Industry

controls Yes Yes Yes Yes Yes Yes Yes Yes

Year

controls Yes Yes Yes Yes Yes Yes Yes Yes

SE clustred

(firm) Yes Yes Yes Yes Yes Yes Yes Yes

N 489 489 489 489 489 489 489 489

R-squared 0.155 0.217 0.235 0.192 0.191 0.220 0.281 0.316

44

Table IV: CEO Appearance and Acquirer Returns around Mergers Announcements

Table IV presents regression analysis of cumulative abnormal returns (relative to the market-

model) surrounding the mergers announcements on the natural logarithm of FAI (Log (FAI)).

Various event windows (-day(s), +day(s)) are reported. Industry is defined as the 2-digit SIC

codes. Standard errors are robust to heteroskedasticity and within firm correlation (clustered

standard errors); t-statistics are reported in the parenthesis where ***, **, and * signify statistical

significance at the 1%, 5%, and 10% levels. The control variables are described in Table I.

Dependent Variable: CAR

(1) (2) (3) (4) (5) (6) (7) (8)

Event window (days)

(-1,-1) (0,0) (-1,1) (-2,-2) (-3,3) (-5,5) (-5,30) (-5,60)

Log (FAI) 0.013 0.027 0.063** 0.120*** 0.109*** 0.131*** 0.206*** 0.314***

(1.510) (1.215) (2.154) (3.388) (3.034) (3.087) (3.848) (4.278)

Log(Size) 0.000 -0.006*** -0.007*** -0.008*** -0.008*** -0.008** -0.022*** -0.017*

(0.049) (-3.027) (-3.100) (-3.124) (-2.738) (-2.471) (-4.160) (-1.934)

MTB 0.000 0.001 0.000 0.000 0.001 0.000 0.001 0.000

(0.694) (1.446) (0.382) (0.347) (0.559) (0.432) (0.380) (0.103)

Stock return 0.002 -0.001 0.007 0.004 -0.004 -0.013* -0.056*** -0.079***

(0.786) (-0.379) (1.129) (0.392) (-0.555) (-1.949) (-3.720) (-4.208)

Leverage 0.003 0.017 0.020 0.022 0.002 -0.008 0.009 0.056

(0.579) (1.259) (1.301) (1.217) (0.095) (-0.317) (0.290) (1.006)

Female -0.018*** -0.031 -0.036* -0.013 -0.003 -0.002 0.013 0.022

(-2.656) (-1.395) (-1.694) (-0.439) (-0.131) (-0.070) (0.536) (0.646)

Age -0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.001

(-0.062) (0.252) (0.780) (0.438) (0.569) (0.113) (0.759) (0.762)

Transaction

value 0.001 -0.001 -0.001 -0.002 -0.003 -0.001 -0.007* -0.003

(1.564) (-1.050) (-0.827) (-0.833) (-1.335) (-0.520) (-1.807) (-0.515)

Public -0.003 -0.022*** -0.027*** -0.031*** -0.034*** -0.035*** -0.036** -0.039*

(-1.169) (-4.571) (-3.908) (-3.892) (-3.933) (-3.840) (-2.339) (-1.940)

45

Initial bid 0.001 -0.005 -0.009 -0.007 -0.007 -0.021* -0.060*** -0.095***

(0.230) (-0.702) (-0.981) (-0.558) (-0.487) (-1.718) (-2.601) (-2.806)

Intercept -0.026 -0.011 -0.079 -0.180** -0.150* -0.186** -0.251** -0.582***

(-1.335) (-0.237) (-1.287) (-2.315) (-1.903) (-1.983) (-2.002) (-3.417)

Industry

controls Yes Yes Yes Yes Yes Yes Yes Yes

Year

controls Yes Yes Yes Yes Yes Yes Yes Yes

SE clustered

(CEO) Yes Yes Yes Yes Yes Yes Yes Yes

N 599 599 599 599 599 599 599 599

R-squared 0.105 0.237 0.228 0.193 0.187 0.200 0.276 0.263

46

Table V: CEO Appearance and Stock Price Reactions around News events

Table V reports regression analysis of cumulative abnormal returns (relative to the market-model)

surrounding television news events and print news events on the natural logarithm of FAI

(Log(FAI)). Various event windows (-day(s), +day(s)) are reported. In Panel A, we report CARs

on both the television and print news events for event windows (-1,-1) and (0,0). In Panel B, we

report CARs around the television news events for longer event windows. Industry is defined

using the 2-digit SIC codes. We search television news stories through Google.coms video

search function. We further restrict the news results to appear only on CNBC.com. We search

print news using the Proquest Complete database; the matched sample of print news stories are

restricted to +/- 10 days surrounding television news events. Television (print) news events that

are within +/- 1 day of print (television) news events are removed. Print news stories that contain

photographs are also removed. Finally, the sample is restricted to news events between 2008 and

2012. Standard errors are robust to heteroskedasticity and within CEO correlation (clustered

standard errors); t-statistics are reported in the parenthesis where ***, **, and * signify statistical

significance at the 1%, 5%, and 10% levels. The control variables are described in Table I.

Panel A. CARs for Both TV and Print News Events

(1) (2) (3) (4)

TV Print TV Print

Event window (days)

(-1,-1) (-1,-1) (0,0) (0,0)

Log (FAI) -0.009 -0.002 0.027** 0.002

(-1.382) (-0.162) (2.556) (0.278)

Log(Size) 0.001 -0.002 -0.002* -0.003*

(1.011) (-1.506) (-1.666) (-1.819)

MTB 0.000 -0.000 0.000 0.000**

(0.888) (-0.777) (1.579) (2.016)

Stock return -0.004*** -0.001 0.000 -0.002

(-3.244) (-0.330) (0.106) (-0.777)

Leverage -0.001 0.012 0.019*** 0.001

(-0.273) (1.258) (3.784) (0.081)

Female 0.001 -0.005 -0.007* -0.004

(0.376) (-0.964) (-1.800) (-0.758)

47

Age -0.000 0.000 0.000 0.000

(-0.284) (1.125) (0.378) (0.289)

Intercept 0.020 -0.013 -0.051** 0.019

(1.357) (-0.381) (-2.471) (1.075)

Industry

controls Yes Yes Yes Yes

Year

controls Yes Yes Yes Yes

SE clustered

(CEO) Yes Yes Yes Yes

N 891 952 891 952

R-squared 0.102 0.068 0.097 0.048

48

Panel B. CARs for TV News Events with Longer Windows

(1) (2) (3) (4) (5) (6)

Event window (days)

(-1,1) (-2,2) (-3,3) (-5,5) (-5,30) (-5,60)

Log (FAI) 0.025* 0.031* 0.023 0.026 -0.013 0.058

(1.788) (1.723) (1.157) (1.069) (-0.226) (0.698)

Log(Size) -0.003 -0.005** -0.007** -0.010*** -0.029** -0.049**

(-1.521) (-2.314) (-2.584) (-3.136) (-2.389) (-2.133)

MTB 0.000 0.000*** 0.000** 0.000** 0.000 0.000

(1.514) (3.740) (2.489) (2.516) (0.893) (1.318)

Stock return -0.002 -0.009** -0.009 -0.023** -0.084*** -0.150***

(-0.866) (-2.301) (-1.217) (-2.497) (-7.534) (-11.101)

Leverage 0.017* 0.018 0.013 0.000 -0.035 -0.066

(1.796) (1.605) (0.993) (0.006) (-1.048) (-1.009)

Female -0.000 -0.007 -0.004 0.003 0.032 0.065

(-0.046) (-0.499) (-0.269) (0.197) (1.156) (1.414)

Age -0.000 0.000 0.000 -0.000 0.000 0.001

(-0.003) (0.288) (0.035) (-0.509) (0.388) (0.561)

Intercept -0.036 -0.022 0.007 0.045 0.286* 0.315

(-0.975) (-0.525) (0.137) (0.852) (1.709) (0.981)

Industry

controls Yes Yes Yes Yes Yes Yes

Year controls Yes Yes Yes Yes Yes Yes

SE Clustered

(CEO) Yes Yes Yes Yes Yes Yes

N 891 891 891 891 891 891

R-squared 0.089 0.127 0.103 0.099 0.149 0.234

49

Table VI: CEO Appearance and Long-run Returns

Table VI reports the relation between FAI and long-run stock returns following CEOs job

announcements and mergers announcements. Panel A reports the average long-run buy and hold

abnormal returns and calendar-time portfolio returns for various event windows following CEOs

job announcements. Panel B reports the average long-run buy and hold abnormal returns and

calendar-time portfolio returns for various event windows following M&A announcements.

Events are sorted into high and low FAI based on the median split. ***, **, and * signify

statistical significance at the 1%, 5%, and 10% levels.

Panel A: Long-run return following CEOs Job Announcement

(1) (2) (3) (4) (5)

Buy and Hold Portfolios Calendar-Time Portfolios

Months FAI N BAHR Alpha

6 High 241 0.041 0.009

6 Low 243 0.080 0.007

p(High - Low) 0.223 0.617

12 High 241 0.104 0.008

12 Low 243 0.145 0.005

p(High - Low) 0.524 0.511

18 High 241 0.154 0.005

18 Low 243 0.183 0.006

p(High - Low) 0.711 0.831

24 High 241 0.163 0.004

24 Low 243 0.209 0.006

p(High - Low) 0.591 0.346

50

Panel B: Long-run returns following Mergers Announcements

(1) (2) (3) (4) (6)

Buy and Hold Calendar-Time Portfolios

Months FAI N BAHR Alpha

6 High 284 0.039 0.009

6 Low 290 -0.008 -0.001

p(High - Low) 0.098* 0.036**

12 High 284 0.119 0.008

12 Low 290 0.033 0.001

p(High - Low) 0.079* 0.047**

18 High 284 0.199 0.006

18 Low 290 0.032 0.0